机器学习中的最优化问题
Optimization Problems for Machine Learning: A Survey
摘要：
本文综述了机器学习文献，并在优化框架中介绍了几种常用的机器学习方法。特别地，数学最优化模型被提出用于回归、分类、聚类、深度学习和对抗学习，以及机器教学、经验模型学习和贝叶斯网络结构学习中的新兴应用。这些模型可以受益于数值优化技术的进步，这些技术已经在几种机器学习环境中发挥了独特的作用。这些模型的优点和缺点进行了讨论，并突出了潜在的研究方向和开放的问题。
英文摘要：
This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
